TensorFlow is an open-source software library developed by Google for numerical computation and machine learning, particularly useful for deep learning applications. It operates using data flow graphs where nodes represent mathematical operations and edges represent data (tensors) that flow between them. The framework enables flexible deployment across CPUs and GPUs and is extensively utilized by various companies for AI and ML solutions.

2. TensorFlow
• Purpose: Machine Learning & deep Learning research
• Open Source (By Google)
• Numerical Computation using data flow graphs

3. Agenda
• Artificial Intelligence
• Machine learning
• Deep Learning
• Why would you use Deep Learning?
• TensorFlow
o TensorFlow Story
o Tensorflow Model
o Data flow graphs
o Architecture
o Portable & scalable
o Programming model
o Companies using TensorFlow

5. AI & ML & DL

6. ArtificialIntelligence
• AI or artificial intelligence : is the simulation of human
intelligence processes by machines, especially computer
systems.
• These processes include learning (the information and
rules for using the information).
• reasoning (using the rules to reach approximate or
definite conclusions).

7. Machine learning
• Machine learning is a type of artificial intelligence (AI)
that provides computers with the ability to learn without
being explicitly programmed.
• Machine learning focuses on
the development of computer
programs that can change
when exposed to new data.

8. Deep Learning
• Deep Learning is a new area of Machine Learning
research, which has been introduced with the objective
of moving Machine Learning closer to one of its original
goals
• that is concerned with emulating the learning approach
that human beings use to gain certain types of
knowledge.

9. Why
would you use
Deep Learning?

10. Gaming

11. Text Recognition
Translation

12. Image Recognition

13. Object recognition

14. Smart Email Reply

15. Voice recognition

16. Self- driving

17. Android Camera
Demo
• Uses a Google Inception model to classify camera
frames in real-time, displaying the top results in an overlay
on the camera image.

19. TensorFlow
• TensorFlow™ is an open source software library for
numerical computation and machine learning.
• Especially useful for Deep learning
• For research and production
• Developed by Google Brain Team

20. TensorFlow Story
• TensorFlow was originally developed by researchers and
engineers working on the Google Brain Team within
Google's Machine Intelligence research organization for
the purposes of conducting machine learning and deep
neural networks research.
• Most Most popular machine learning library.
o Over 32,000 downlowds

21. Tensorflow Model
• Big idea: Express a numeric computation as a graph.
• Nodes = Mathematical Operations
o Nodes in the graph represent mathematical operations,
• Data Edges = Multi-Dimensional Arrays
(Tensor)
o communicated between them
• Data (Tensors) flow through the graph

22. Data flow graphs
• Computation is defined
as directed a cyclic graph (GDA)
o Optimize an objective function.
• Graph is defined in high-level
Language (python, C++,Go)
• Graph is compiled and optimized
• Graph is executed (parts or fully)
On level devices (CPU, GPU).

23. Architecture
• Core in C++
• Front end code python and C++.
• The flexible architecture allows you to deploy
computation to one or more CPUs or GPUs

24. Portable & scalable
• deploy computation in a desktop, server, or mobile
device with a single API

25. Programming model
1.Build a graph
a. Graph contains parameter specifications, model architecture, optimization
process, …
b. Somewhere between 5 and 5000 lines
2.Initialize a session
3.Fetch and feed data with Session.run
a. Compilation, optimization, etc. happens at this step — you probably won’t
notice

26. Programming model
(Build a graph)
• Variables are 0-ary stateful
nodes which output their
current value.
• Placeholders are 0-ary
nodes whose value is fed in at
execution time.
• Mathematical operations:
o MatMul: Multiply two matrix
values.
o Add: Add elementwise (with
broadcasting).
o ReLU: Activate with elementwise
rectified linear function.

27. Programming model
(Build a graph)
• import tensorflow as tf
b = tf.Variable(tf.zeros((100,)))
W=tf.Variable(tf.random_uniform((784,
100), -1, 1))
x = tf.placeholder(tf.float32, (None,
784))
h_i = tf.nn.relu(tf.matmul(x, W) + b)

28. Programming model
(Initialize & Run session)
• a session: a binding to a particular execution context
sess.run(fetches, feeds)
Fetches: List of graph nodes. Return the outputs of these
nodes.
Feeds: Dictionary mapping from graph nodes to concrete
values. Specifies the value of each graph node given in
the dictionary.

29. Programming model
(Build a graph)
import numpy as np
import tensorflow as tf
b = tf.Variable(tf.zeros((100,)))
W = tf.Variable(tf.random_uniform((784, 100),
-1, 1))
x = tf.placeholder(tf.float32, (None, 784))
h_i = tf.nn.relu(tf.matmul(x, W) + b)
-----Initial and Run Session-------
sess = tf.Session()
sess.run(tf.initialize_all_variables())
sess.run(h_i, {x: np.random.random(64, 784)})

30. Companies using
TensorFlow

31. https://www.facebook.com/WTM.Cairo/
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